Disable (pause) an API test so it stops running on its schedule. Its state becomes PAUSED and no new runs are recorded until you enable_api_test it again. Returns the updated API test.
| Name | Required | Description | Default |
|---|---|---|---|
| apiTestId | Yes | Id of the API test to disable |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and succeeds: it discloses the state change to PAUSED, the side effect of no new runs being recorded, reversibility via enable_api_test, and the return value (updated API test). This gives the agent a complete behavioral model beyond the bare operation name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded: the verb and object appear immediately, and the two sentences provide only necessary details (state, schedule impact, reversibility, return). No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one well-documented parameter and no output schema or annotations, the description covers all relevant aspects: purpose, effect, reversibility, and return value. It is complete enough for an agent to decide when to call it and what to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents the single parameter apiTestId with a clear description ('Id of the API test to disable'), and schema coverage is 100%. The tool description adds no extra meaning or format details, so it meets the baseline for high schema coverage without surpassing it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Disable (pause)') and the specific resource ('API test'), with the scope of 'stops running on its schedule.' It distinguishes itself from siblings like enable_api_test and delete_api_test by explicitly describing the paused state and the behavior of not recording new runs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: use when you want to pause an API test, and it explicitly references enable_api_test for resuming, which hints at the alternative. However, it does not explicitly exclude destructive alternatives like delete_api_test or state when this is preferred over deletion, so it stops short of full usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Several tool pairs are near-duplicates, including three deprecated aliases (add_investigation_alert_channel vs add_alert_channel, list_investigation_alert_channels vs list_alert_channels, remove_investigation_alert_channel vs remove_alert_channel) that muddy the surface. Additionally, suppress_signal and create_ignore_rule both suppress alerting via different mechanisms, which could cause misselection despite detailed descriptions.
The vast majority of tools follow a clear verb_noun snake_case pattern (create_api_test, list_issues, set_alert_rule_status). A few bare-noun tools (logs, spans, metrics) and the standalone verb correlate break the pattern slightly, but overall the naming is highly consistent and predictable.
With 52 tools, this is on the extreme end of the calibration scale. Even accounting for the broad scope of an observability platform, the count is excessive and includes several deprecated redundancies that inflate it further.
The toolset provides comprehensive CRUD/lifecycle coverage across all major domains: alert rules (create, read, update, delete, status, delivery, preview), API tests (create, read, update, delete, run history, credentials), ignore rules and suppressions, issues with digest config, investigations with claim/read, channels, credentials, and rich query tools (logs, spans, metrics, SQL, traces, correlation). No obvious dead ends or missing core operations.